Find flow-based communities in complex networks

C3 generates distinct, deterministic categorical colors for maps and charts. Version 2 places hues on the OKLCH hue circle or along any d3 color scheme, and adds tones, seed colors, and nested colors for hierarchies, with each child varying within its parent's range. It now comes in TypeScript (npm install @mapequation/c3) and Python (pip install c3-colors) with the same functions and the same colors, and has an interactive explorer (changelog).

infomap.run() is now the canonical, fully typed entry point for community detection — common options live on the signature, and results come back as rich Result objects with summary(), to_series(), and one-call to_networkx() and to_igraph() conversions, plus a summary card that renders inline in notebooks. Also new: bundled example networks in infomap.datasets, named state nodes across the Python and R APIs, the infomap-network v1.0 JSON input format, and exposed flow-iteration and tolerance controls. Options are now validated with guided error messages, and engine logging routes through Python's standard logging module (Python docs, changelog).

The map equation tutorial interactively explains the mechanics of the Map equation. In his master's thesis work, Henrik Larsson did a major update of the whole app and added missing sections explaining the hierarchical map equation and regularization for incomplete data.

Stop trials on a codelength plateau with --converge, compact --multilayer-relax-to-self coupling, multilayer cluster-data in the Python/R API, cooperative interrupt hooks for long-running runs, and pretty console output. Million-scale runs now use roughly half the peak memory and finish about 25% faster than v2.12 with no loss in solution quality, and peak memory is now largely independent of hierarchy depth — so deep hierarchical networks are far cheaper to cluster (changelog).

Automatic thread counts for clusters and schedulers, reproducible run metadata, JSON run reports, distributed trial sharding, concurrent trials, a Python GraphRAG adapter, and lower peak memory for multi-trial runs (HPC notebook, changelog).